OpenRaiser/Intern-Atlas huggingface dataset:
Vortex: Efficient Sample-Free Dynamic Tensor Program Optimization via Hardware-aware Strategy Space Hierarchization
Venue: arXiv cs.DC 2024
Year: 2024

Dynamic-shape deep neural networks (DNNs) are rapidly evolving, attracting attention for their ability to handle variable input sizes in real-time applications. However, existing compilation optimization methods for such networks often rely heavily on predefined samples to guide the compilation process, which restricts their adaptability and efficiency. These sample-driven methods struggle to efficiently manage the diverse and unpredictable shapes encountered in real-world scenarios, often resulting in suboptimal performance. To tackle these issues, we introduce Vortex, a hardware-driven and sample-free compiler tailored for dynamic-shape tensor programs. Vortex capitalizes on detailed hardware information and hierarchizes the strategy space to facilitate high-performance code generation without relying on runtime shape samples. It features a unique bidirectional compilation workflow, combining top-down abstraction for aligning tensor program execution with hardware hierarchies and bottom-up kernel construction to narrow the search space, enabling Vortex to achieve remarkable efficiency. Comprehensive evaluations confirm that Vortex reduces compilation time by $176\times$ compared to the existing dynamic-shape compiler. Additionally, it substantially outperforms existing vendor-provided libraries and dynamic-shape compilers on both CPU and GPU platforms, delivering speedups of $2.53\times$ and $3.01\times$, respectively.
Online One-Dimensional Magnetic Field SLAM with Loop-Closure Detection
Venue: arXiv cs.RO 2024
Year: 2024

We present a lightweight magnetic field simultaneous localisation and mapping (SLAM) approach for drift correction in odometry paths, where the interest is purely in the odometry and not in map building. We represent the past magnetic field readings as a one-dimensional trajectory against which the current magnetic field observations are matched. This approach boils down to sequential loop-closure detection and decision-making, based on the current pose state estimate and the magnetic field. We combine this setup with a path estimation framework using an extended Kalman smoother which fuses the odometry increments with the detected loop-closure timings. We demonstrate the practical applicability of the model with several different real-world examples from a handheld iPad moving in indoor scenes.
SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Out-of-Distribution (OOD) detection in computer vision is a crucial research area, with related benchmarks playing a vital role in assessing the generalizability of models and their applicability in real-world scenarios. However, existing OOD benchmarks in the literature suffer from two main limitations: (1) they often overlook semantic shift as a potential challenge, and (2) their scale is limited compared to the large datasets used to train modern models. To address these gaps, we introduce SOOD-ImageNet, a novel dataset comprising around 1.6M images across 56 classes, designed for common computer vision tasks such as image classification and semantic segmentation under OOD conditions, with a particular focus on the issue of semantic shift. We ensured the necessary scalability and quality by developing an innovative data engine that leverages the capabilities of modern vision-language models, complemented by accurate human checks. Through extensive training and evaluation of various models on SOOD-ImageNet, we showcase its potential to significantly advance OOD research in computer vision. The project page is available at https://github.com/bach05/SOODImageNet.git.
Large Language Models Can Understanding Depth from Monocular Images
Venue: arXiv cs.CV 2024
Year: 2024

Monocular depth estimation is a critical function in computer vision applications. This paper shows that large language models (LLMs) can effectively interpret depth with minimal supervision, using efficient resource utilization and a consistent neural network architecture. We introduce LLM-MDE, a multimodal framework that deciphers depth through language comprehension. Specifically, LLM-MDE employs two main strategies to enhance the pretrained LLM's capability for depth estimation: cross-modal reprogramming and an adaptive prompt estimation module. These strategies align vision representations with text prototypes and automatically generate prompts based on monocular images, respectively. Comprehensive experiments on real-world MDE datasets confirm the effectiveness and superiority of LLM-MDE, which excels in few-/zero-shot tasks while minimizing resource use. The source code is available.
DTRAN: A Special Use Case of RAN Optimization using Digital Twin
Venue: arXiv cs.NI 2024
Year: 2024

The emergence of beyond 5G (B5G) and 6G networks underscores the critical role of advanced computer-aided tools, such as network digital twins (DTs), in fostering autonomous networks and ubiquitous intelligence. Existing solutions in the DT domain primarily aim to model and automate specific tasks within the network lifecycle, which lack flexibility and adaptability for fully autonomous design and management. Unlike the existing DT approaches, we propose RAN optimization using the Digital Twin (DTRAN) framework that follows a holistic approach from core to edge networks. The proposed DTRAN framework enables real-time data management and communication with the physical network, which provides a more accurate and detailed digital replica than the existing approaches. We outline the main building blocks of the DTRAN and describe the details of our specific use case, which is RAN configuration optimization, to demonstrate the applicability of the proposed framework for a real-world scenario.
PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery
Venue: arXiv cs.CV 2024
Year: 2024

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed; and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery; during live surgery; and when writing operation notes. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision; and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://doi.org/10.5522/04/26531686.
Backdoor Defense through Self-Supervised and Generative Learning
Venue: arXiv cs.LG 2024
Year: 2024

Backdoor attacks change a small portion of training data by introducing hand-crafted triggers and rewiring the corresponding labels towards a desired target class. Training on such data injects a backdoor which causes malicious inference in selected test samples. Most defenses mitigate such attacks through various modifications of the discriminative learning procedure. In contrast, this paper explores an approach based on generative modelling of per-class distributions in a self-supervised representation space. Interestingly, these representations get either preserved or heavily disturbed under recent backdoor attacks. In both cases, we find that per-class generative models allow to detect poisoned data and cleanse the dataset. Experiments show that training on cleansed dataset greatly reduces the attack success rate and retains the accuracy on benign inputs.
Towards General Industrial Intelligence: A Survey of Continual Large Models in Industrial IoT
Venue: arXiv cs.LG 2024
Year: 2024

Industrial AI is transitioning from traditional deep learning models to large-scale transformer-based architectures, with the Industrial Internet of Things (IIoT) playing a pivotal role. IIoT evolves from a simple data pipeline to an intelligent infrastructure, enabling and enhancing these advanced AI systems. This survey explores the integration of IIoT with large models (LMs) and their potential applications in industrial environments. We focus on four primary types of industrial LMs: language-based, vision-based, time-series, and multimodal models. The lifecycle of LMs is segmented into four critical phases: data foundation, model training, model connectivity, and continuous evolution. First, we analyze how IIoT provides abundant and diverse data resources, supporting the training and fine-tuning of LMs. Second, we discuss how IIoT offers an efficient training infrastructure in low-latency and bandwidth-optimized environments. Third, we highlight the deployment advantages of LMs within IIoT, emphasizing IIoT's role as a connectivity nexus fostering emergent intelligence through modular design, dynamic routing, and model merging to enhance system scalability and adaptability. Finally, we demonstrate how IIoT supports continual learning mechanisms, enabling LMs to adapt to dynamic industrial conditions and ensure long-term effectiveness. This paper underscores IIoT's critical role in the evolution of industrial intelligence with large models, offering a theoretical framework and actionable insights for future research.
A Review of Image Retrieval Techniques: Data Augmentation and Adversarial Learning Approaches
Venue: arXiv cs.CV 2024
Year: 2024

Image retrieval is a crucial research topic in computer vision, with broad application prospects ranging from online product searches to security surveillance systems. In recent years, the accuracy and efficiency of image retrieval have significantly improved due to advancements in deep learning. However, existing methods still face numerous challenges, particularly in handling large-scale datasets, cross-domain retrieval, and image perturbations that can arise from real-world conditions such as variations in lighting, occlusion, and viewpoint. Data augmentation techniques and adversarial learning methods have been widely applied in the field of image retrieval to address these challenges. Data augmentation enhances the model's generalization ability and robustness by generating more diverse training samples, simulating real-world variations, and reducing overfitting. Meanwhile, adversarial attacks and defenses introduce perturbations during training to improve the model's robustness against potential attacks, ensuring reliability in practical applications. This review comprehensively summarizes the latest research advancements in image retrieval, with a particular focus on the roles of data augmentation and adversarial learning techniques in enhancing retrieval performance. Future directions and potential challenges are also discussed.
The Adjacent Fragment and Quine's Limits of Decision
Venue: arXiv cs.LO 2024
Year: 2024

We introduce the adjacent fragment AF of first-order logic, obtained by restricting the sequences of variables occurring as arguments in atomic formulas. The adjacent fragment generalizes (after a routine renaming) the two-variable fragment of first-order logic as well as the so-called fluted fragment. We show that the adjacent fragment has the finite model property, and that the satisfiability problem for its k-variable sub-fragment is in (k-1)-NExpTime. Using known results on the fluted fragment, it follows that the satisfiability problem for the whole adjacent fragment is Tower-complete. We additionally consider the effect of the adjacency requirement on the well-known guarded fragment of first-order logic, whose satisfiability problem is TwoExpTime-complete. We show that the satisfiability problem for the intersection of the adjacent and guarded adjacent fragments remains TwoExpTime-hard. Finally, we show that any relaxation of the adjacency condition on the allowed order of variables in argument sequences yields a logic whose satisfiability and finite satisfiability problems are undecidable.
Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning
Venue: arXiv cs.LG 2024
Year: 2024

Machine Learning (ML) is crucial in many sectors, including computer vision. However, ML models trained on sensitive data face security challenges, as they can be attacked and leak information. Privacy-Preserving Machine Learning (PPML) addresses this by using Differential Privacy (DP) to balance utility and privacy. This study identifies image dataset characteristics that affect the utility and vulnerability of private and non-private Convolutional Neural Network (CNN) models. Through analyzing multiple datasets and privacy budgets, we find that imbalanced datasets increase vulnerability in minority classes, but DP mitigates this issue. Datasets with fewer classes improve both model utility and privacy, while high entropy or low Fisher Discriminant Ratio (FDR) datasets deteriorate the utility-privacy trade-off. These insights offer valuable guidance for practitioners and researchers in estimating and optimizing the utility-privacy trade-off in image datasets, helping to inform data and privacy modifications for better outcomes based on dataset characteristics.
Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&A
Venue: arXiv cs.AI 2024
Year: 2024

Large language models struggle to synthesize disparate pieces of information into a coherent plan when approaching a complex procedural task. In this work, we introduce a novel formalism and structure for such procedural knowledge. Based on this formalism, we present a novel procedural knowledge dataset called LCStep, which we created from LangChain tutorials. To leverage this procedural knowledge to solve new tasks, we propose analogy-augmented generation (AAG), which draws inspiration from the human ability to assimilate past experiences to solve unfamiliar problems. AAG uses a custom procedure memory store to retrieve and adapt specialized domain knowledge to answer new procedural tasks. We demonstrate that AAG outperforms few-shot and RAG baselines on LCStep, RecipeNLG, and CHAMP datasets under a pairwise LLM-based evaluation, corroborated by human evaluation in the case of RecipeNLG.
CHESS: Optimizing LLM Inference via Channel-Wise Thresholding and Selective Sparsification
Venue: arXiv cs.CL 2024
Year: 2024

Deploying large language models (LLMs) on edge devices presents significant challenges due to the substantial computational overhead and memory requirements. Activation sparsification can mitigate these resource challenges by reducing the number of activated neurons during inference. Existing methods typically employ thresholding-based sparsification based on the statistics of activation tensors. However, they do not model the impact of activation sparsification on performance, resulting in suboptimal performance degradation. To address the limitations, this paper reformulates the activation sparsification problem to explicitly capture the relationship between activation sparsity and model performance. Then, this paper proposes CHESS, a general activation sparsification approach via CHannel-wise thrEsholding and Selective Sparsification. First, channel-wise thresholding assigns a unique threshold to each activation channel in the feed-forward network (FFN) layers. Then, selective sparsification involves applying thresholding-based activation sparsification to specific layers within the attention modules. Finally, we detail the implementation of sparse kernels to accelerate LLM inference. Experimental results demonstrate that the proposed CHESS achieves lower performance degradation over eight downstream tasks while activating fewer parameters than existing methods, thus speeding up the LLM inference by up to 1.27x.
3D-LSPTM: An Automatic Framework with 3D-Large-Scale Pretrained Model for Laryngeal Cancer Detection Using Laryngoscopic Videos
Venue: arXiv cs.CV 2024
Year: 2024

Laryngeal cancer is a malignant disease with a high morality rate in otorhinolaryngology, posing an significant threat to human health. Traditionally larygologists manually visual-inspect laryngeal cancer in laryngoscopic videos, which is quite time-consuming and subjective. In this study, we propose a novel automatic framework via 3D-large-scale pretrained models termed 3D-LSPTM for laryngeal cancer detection. Firstly, we collect 1,109 laryngoscopic videos from the First Affiliated Hospital Sun Yat-sen University with the approval of the Ethics Committee. Then we utilize the 3D-large-scale pretrained models of C3D, TimeSformer, and Video-Swin-Transformer, with the merit of advanced featuring videos, for laryngeal cancer detection with fine-tuning techniques. Extensive experiments show that our proposed 3D-LSPTM can achieve promising performance on the task of laryngeal cancer detection. Particularly, 3D-LSPTM with the backbone of Video-Swin-Transformer can achieve 92.4% accuracy, 95.6% sensitivity, 94.1% precision, and 94.8% F_1.
Phantom: Untargeted Poisoning Attacks on Semi-Supervised Learning (Full Version)
Venue: arXiv cs.CR 2024
Year: 2024

Deep Neural Networks (DNNs) can handle increasingly complex tasks, albeit they require rapidly expanding training datasets. Collecting data from platforms with user-generated content, such as social networks, has significantly eased the acquisition of large datasets for training DNNs. Despite these advancements, the manual labeling process remains a substantial challenge in terms of both time and cost. In response, Semi-Supervised Learning (SSL) approaches have emerged, where only a small fraction of the dataset needs to be labeled, leaving the majority unlabeled. However, leveraging data from untrusted sources like social networks also creates new security risks, as potential attackers can easily inject manipulated samples. Previous research on the security of SSL primarily focused on injecting backdoors into trained models, while less attention was given to the more challenging untargeted poisoning attacks. In this paper, we introduce Phantom, the first untargeted poisoning attack in SSL that disrupts the training process by injecting a small number of manipulated images into the unlabeled dataset. Unlike existing attacks, our approach only requires adding few manipulated samples, such as posting images on social networks, without the need to control the victim. Phantom causes SSL algorithms to overlook the actual images' pixels and to rely only on maliciously crafted patterns that \ourname superimposed on the real images. We show Phantom's effectiveness for 6 different datasets and 3 real-world social-media platforms (Facebook, Instagram, Pinterest). Already small fractions of manipulated samples (e.g., 5\%) reduce the accuracy of the resulting model by 10\%, with higher percentages leading to a performance comparable to a naive classifier. Our findings demonstrate the threat of poisoning user-generated content platforms, rendering them unsuitable for SSL in specific tasks.
Watermarking of Quantum Circuits
Venue: arXiv cs.CR 2024
Year: 2024

Quantum circuits constitute Intellectual Property (IP) of the quantum developers and users, which needs to be protected from theft by adversarial agents, e.g., the quantum cloud provider or a rogue adversary present in the cloud. This necessitates the exploration of low-overhead techniques applicable to near-term quantum devices, to trace the quantum circuits/algorithms\textquotesingle{} IP and their output. We present two such lightweight watermarking techniques to prove ownership in the event of an adversary cloning the circuit design. For the first technique, a rotation gate is placed on ancilla qubits combined with other gate(s) at the output of the circuit. For the second method, a set of random gates are inserted in the middle of the circuit followed by its inverse, separated from the circuit by a barrier. These models are combined and applied on benchmark circuits, and the circuit depth, 2-qubit gate count, probability of successful trials (PST), and probabilistic proof of authorship (PPA) are compared against the state-of-the-art. The PST is reduced by a minuscule 0.53\% against the non-watermarked benchmarks and is up to 22.69\% higher compared to existing techniques. The circuit depth has been reduced by up to 27.7\% as against the state-of-the-art. The PPA is astronomically smaller than existing watermarks.
Situation-aware Autonomous Driving Decision Making with Cooperative Perception on Demand
Venue: arXiv cs.RO 2024
Year: 2024

This paper investigates the impact of cooperative perception on autonomous driving decision making on urban roads. The extended perception range contributed by the cooperative perception can be properly leveraged to address the implicit dependencies within the vehicles, thereby the vehicle decision making performance can be improved. Meanwhile, we acknowledge the inherent limitation of wireless communication and propose a Cooperative Perception on Demand (CPoD) strategy, where the cooperative perception will only be activated when the extended perception range is necessary for proper situation-awareness. The situation-aware decision making with CPoD is modeled as a Partially Observable Markov Decision Process (POMDP) and solved in an online manner. The evaluation results demonstrate that the proposed approach can function safely and efficiently for autonomous driving on urban roads.
METcross: A framework for short-term forecasting of cross-city metro passenger flow
Venue: arXiv cs.CY 2024
Year: 2024

Metro operation management relies on accurate predictions of passenger flow in the future. This study begins by integrating cross-city (including source and target city) knowledge and developing a short-term passenger flow prediction framework (METcross) for the metro. Firstly, we propose a basic framework for modeling cross-city metro passenger flow prediction from the perspectives of data fusion and transfer learning. Secondly, METcross framework is designed to use both static and dynamic covariates as inputs, including economy and weather, that help characterize station passenger flow features. This framework consists of two steps: pre-training on the source city and fine-tuning on the target city. During pre-training, data from the source city trains the feature extraction and passenger flow prediction models. Fine-tuning on the target city involves using the source city's trained model as the initial parameter and fusing the feature embeddings of both cities to obtain the passenger flow prediction results. Finally, we tested the basic prediction framework and METcross framework on the metro networks of Wuxi and Chongqing to experimentally analyze their efficacy. Results indicate that the METcross framework performs better than the basic framework and can reduce the Mean Absolute Error and Root Mean Squared Error by 22.35% and 26.18%, respectively, compared to single-city prediction models.